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Fading memory and kernel properties of generic cortical microcircuit models

Publications associées (52)

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Small signal modeling of charge and piezoresistive modulations in active MEM resonators

Mihai Adrian Ionescu, Dimitrios Tsamados, Daniel Grogg, Suat Ayoz

In this work we establish novel analytical and small signal circuit modeling of two major effects that co-exist in active Micro-Electro-Mechanical resonators: charge and piezoresistance modulations. Analytical expressions for the output current of vibratin ...

2009

Fault-Tolerance and Reliability of Post-CMOS Systems: a Circuit Perspective

Alexandre Schmid, Yusuf Leblebici, Milos Stanisavljevic

The reliability of systems made of unreliable nanoelectronic devices is discussed in this paper. Massive defect density which may affect future fabrication technologies calls for novel solutions, where spatial redundancy and the voting scheme play a signif ...

IEEE Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2009

Activelets and sparsity: A new way to detect brain activation from fMRI data - art. no. 67010Y

Michaël Unser, Dimitri Nestor Alice Van De Ville, Ildar Khalidov

FMRI time course processing is traditionally performed using linear regression followed by statistical hypothesis testing. While this analysis method is robust against noise, it relies strongly on the signal model. In this paper, we propose a non-parametri ...

Spie-Int Soc Optical Engineering, Po Box 10, Bellingham, Wa 98227-0010 Usa2007

Activelets and Sparsity: A New Way to Detect Brain Activation from fMRI Data

Michaël Unser, Dimitri Nestor Alice Van De Ville, Ildar Khalidov

SPIE2007

Hierarchical Penalization

Hierarchical penalization is a generic framework for incorporating prior information in the fitting of statistical models, when the explicative variables are organized in a hierarchical structure. The penalizer is a convex functional that performs soft sel ...

2007

Hierarchical Penalization

IDIAP2007

Spiking Neuron Networks A survey

Spiking Neuron Networks (SNNs) are often referred to as the 3rd generation of neural networks. They derive their strength and interest from an accurate modelling of synaptic interactions between neurons, taking into account the time of spike emission. SNNs ...

IDIAP2006

Attractor learning with nonlinear, artificial, neural network

A nonlinear recurrent neural network is trained to synthesize chaotic signals. The identification process is reduced to a teaching phase and a linear regression. The influence of the shape of the nonlinearity in the neurons and the noise amplitude are stud ...

2003

Input prediction and autonomous movement analysis in recurrent circuits of spiking neurons

Henry Markram

Temporal integration of information and prediction of future sensory inputs are assumed to be important computational tasks of generic cortical microcircuits. It has remained open how cortical microcircuits could possibly achieve this, especially since the ...

2003

Analogue VLSI building blocks for an electronic auditory pathway

This thesis gives an overview of my work over the last four years on the development of analogue electronic building blocks for the auditory pathway, and their application to some models of processing in the auditory brainstem. The anatomy and physiology o ...

EPFL1998